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The master thesis will focus on applying, evaluating, and improving an existing MBSE Copilot solution within a real-world engineering context. The tasks are:

Analyze Copilot capabilities for supporting MBSE workflows, including requirement handling and model generation
Conduct a case study using representative MBSE artifacts (e.g., SysML models, requirement documents) to assess tool performance
Perform user testing to evaluate usability, efficiency gains, and acceptance among engineers
Investigate input quality and segmentation strategies for large-scale requirements (e.g., 600+ pages) to ensure meaningful Copilot output
Benchmark Copilot performance against traditional manual MBSE processes to determine effectiveness and identify improvement areas
Document findings and propose enhancements for better integration of AI copilots in MBSE lifecycles

Strong MBSE knowledge: Familiarity with model-based systems engineering principles and SysML (ideally SysML v2)
AI/ML understanding: Basic concepts of artificial intelligence and machine learning, especially in engineering applications
Analytical and evaluation skills: Ability to assess tool performance, identify improvement areas, and interpret user feedback
Technical proficiency: Basic programming skills for data analysis and automation tasks
Tool familiarity: Experience with MBSE tools such as Cameo or Capella is advantageous but not mandatory
Communication and documentation: Ability to clearly document findings and present results to technical and non-technical stakeholders
Research capability: Competence in conducting literature reviews and synthesizing insights from academic and industrial sources

nice to have:

Experience with SysML v2
Familiarity with MBSE tools (Cameo, Capella)
Knowledge of Python or AI frameworks
Prior experience in user studies or usability testing

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